This repository contains two machine learning models trained to predict breast cancer diagnosis based on the Breast Cancer Wisconsin (Diagnostic) dataset. The dataset consists of features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass, and the target variable indicates whether the mass is benign or malignant.
Model 1 is a logistic regression classifier trained on the dataset. Logistic regression is a popular method for binary classification tasks like this one. The model achieves a certain level of accuracy in predicting breast cancer diagnosis based on the input features.
Model 2 is a Random Forest classifier trained on the same dataset. Random Forest is an ensemble learning method that builds multiple decision trees and merges their predictions to improve accuracy and reduce overfitting. This model provides an alternative approach to predicting breast cancer diagnosis.